Confidence Intervals

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When we wrote Quantifying the User Experience, we put confidence intervals before tests of statistical significance. We generally find fluency with confidence intervals to be easier to achieve and of more value than with formal hypothesis testing. We also teach confidence intervals in our workshops on statistical methods. Most people, even non-researchers, have been exposed to the concept of margins of error—political polls include them.

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The Net Promoter Score (NPS) is a widely used metric, but it can be tricky to work with statistically. One of the first statistical steps we recommend that researchers take is to add confidence intervals around their metrics. Confidence intervals provide a good visualization of how precise estimates from samples are. They are particularly helpful in longitudinal research to help differentiate the inevitable fluctuations in

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Every estimate we make from a sample of customer data contains error. Confidence intervals tell us how much faith we can have in our estimates. Confidence intervals quantify the most likely range for the unknown value we're estimating. For example, if we observe 27 out of 30 users (90%) completing a task, we can be 95% confident that between 74% and 97% of all real-world

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Confidence intervals are your frenemies. They are one of the most useful statistical techniques you can apply to customer data. At the same time they can be perplexing and cumbersome. But confidence intervals provide an essential understanding of how much faith we can have in our sample estimates, from any sample size, from 2 to 2 million.  They provide the most likely range for the

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You don't need a PhD in statistics to understand and use confidence intervals. Because we almost always sample a fraction of the users from a larger population, there is uncertainty in our estimates. Confidence intervals are an excellent way of understanding the role of sampling error in the averages and percentages that are ubiquitous in user research. Confidence intervals tell you the most likely range

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Adding confidence intervals to completion rates in usability tests will temper both excessive skepticism and overstated usability findings. Confidence intervals make testing more efficient by quickly revealing unusable tasks with very small samples. Examples are detailed and downloadable calculators are available.   Are you Lacking Confidence?   You just finished a usability test. You had 5 participants attempt a task in a new version of

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